Applying machine learning techniques to predict the properties of energetic materials
نویسندگان
چکیده
منابع مشابه
Using Machine Learning ARIMA to Predict the Price of Cryptocurrencies
The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average(ARIMA) model ...
متن کاملApplying machine learning techniques to ecological data
This thesis is about modelling carbon flux in forests based on meterological variables using modern machine learning techniques. The motivation is to better understand the carbon uptake process from trees and find the driving factors of it, using totally automated techniques. Data from two British forests were used, (Griffin and Harwood) but finally results were obtained only with Harwood becau...
متن کاملApplying Machine Learning Techniques to ASP Solving
Having in mind the task of improving the solving methods for Answer Set Programming (ASP), there are two usual ways to reach this goal: (i) extending state-of-the-art techniques and ASP solvers, or (ii) designing a new ASP solver from scratch. An alternative to these trends is to build on top of state-of-the-art solvers, and to apply machine learning techniques for choosing automatically the “b...
متن کاملMachine Learning Techniques to Predict Software Defect
Machine learning techniques have been dominating in the last two decades. The recently published comprehensive state-of-the-art review (Mohanty et al., 2010) justifies this issue. The ability of software quality models to accurately identify critical faulty components allows for the application of focused verification activities ranging from manual inspection to automated formal analysis method...
متن کاملApplying Different Machine Learning Models to Predict Breast Cancer Risk
In this paper, we apply five machine learning models (Logistic Regression, Naive Bayes, LinearSVC, SVM with linear kernel and Random Forest) and three feature selection techniques (PCA, RFE and Heatmap) in one of the key procedures for breast cancer diagnosis. Using the biopsy cytopathology data with 30 numerical features, we achieve a high accuracy of 97.8%. We further compare performances of ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Scientific Reports
سال: 2018
ISSN: 2045-2322
DOI: 10.1038/s41598-018-27344-x